Wind turbine generator slip ring damage detection through temperature data analysis
| 1 | University of Perugia - Department of Engineering |
CORRESPONDING AUTHOR
Submission date: 2019-03-03
Final revision date: 2019-06-06
Acceptance date: 2019-06-10
Online publication date: 2019-06-17
Publication date: 2019-06-17
Diagnostyka 2019;20(3):3–9
KEYWORDS
TOPICS
ABSTRACT
The use of condition monitoring techniques in wind energy has been recently growing and the average unavailability time of an operating wind turbine in an industrial wind farm is estimated to be less than the 3%. The most powerful approach for gearbox condition monitoring is vibration analysis, but it should be noticed as well that the collected data are complex to analyse and interpret and that the measurement equipment is costly. For these reasons, several wind turbine subcomponents are monitored through temperature sensors. It is therefore valuable developing analysis techniques for this kind of data, with the aim of detecting incoming faults as early as possible. On these grounds, the present work is devoted to a test case study of wind turbine generator slip ring damage detection. A principal component regression is adopted, targeting the temperature collected at the slip ring. Using also the data collected at the nearby wind turbines in the farm, it is possible to identify the incoming fault approximately one day before it occurs.
REFERENCES (41)
1.
Saenz-Aguirre A, Zulueta, E Fernandez-Gamiz U, Lozano J, Lopez-Guede JM. Artificial neural network based reinforcement learning for wind turbine yaw control. Energies, 2019;12(3): 436. https://doi.org/10.3390/en1203....
2.
Astolfi D, Castellani F, Terzi L. Wind turbine power curve upgrades. Energies. 2018; 11(5):1300. https://doi.org/10.3390/en1105....
3.
Lee G, Ding Y, Xie L, Genton MG. A kernel plus method for quantifying wind turbine performance upgrades. Wind Energy, 2015;18(7):1207-1219. https://doi.org/10.1002/we.175....
4.
Astolfi D, Castellani F, Lombardi A, Terzi L. About the extension of wind turbine power curve in the high wind region. Journal of Solar Energy Engineering. 2019; 141(1): 014501. https://doi.org/10.1115/1.4041....
5.
Petrović V, Bottasso CL. Wind turbine envelope protection control over the full wind speed range. Renewable energy. 2017; 111: 836-848. https://doi.org/10.1016/j.rene....
6.
Wang F, Garcia-Sanz M. Wind farm cooperative control for optimal power generation. Wind Engineering. 2018; 42(6): 547-560. https://doi.org/10.1177/030952....
7.
Park J, Law KH. Cooperative wind turbine control for maximizing wind farm power using sequential convex programming. Energy Conversion and Management. 2015; 101: 295-316. https://doi.org/10.1016/j.enco....
8.
Fleming P, Annoni J, Shah JJ, Wang L, Ananthan S, Zhang Z, Chen L. Field test of wake steering at an offshore wind farm. Wind Energy Science. 2017; 2(1), 229-239. https://doi.org/10.5194/wes-2-....
9.
Tchakoua P, Wamkeue R, Ouhrouche M, Slaoui-Hasnaoui F, Tameghe T, Ekemb G. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies. 2014; 7(4): 2595-2630. https://doi.org/10.3390/en7042....
10.
Feng Y, Qiu Y, Crabtree CJ, Long H, Tavner PJ. Monitoring wind turbine gearboxes. Wind Energy. 2013; 16(5), 728-740. https://doi.org/10.1002/we.152....
11.
Sheng S. Wind turbine gearbox condition monitoring round robin study-vibration analysis (No. NREL/TP-5000-54530). National Renewable Energy Lab.(NREL), Golden, CO (United States). 2012. https://doi.org/10.2172/104898....
12.
Lin Y, Tu L, Liu H, Li W. Fault analysis of wind turbines in China. Renewable and Sustainable Energy Reviews. 2016; 55: 482-490. https://doi.org/10.1016/j.rser....
13.
Astolfi D, Castellani F, Terzi L. Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. 2014; Diagnostyka, 15.
14.
Astolfi D, Scappaticci L, Terzi L. Fault diagnosis of wind turbine gearboxes through temperature and vibration data. International Journal of Renewable Energy Research (IJRER). 2017; 7(2): 965-976.
15.
Guo P, Infield D, Yang X. Wind turbine generator condition-monitoring using temperature trend analysis. IEEE Transactions on sustainable energy. 2012; 3(1):124-133. https://doi.org/10.1109/TSTE.2....
16.
Zaher ASAE, McArthur SDJ, Infield DG, Patel Y. Online wind turbine fault detection through automated SCADA data analysis. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology. 2009; 12(6): 574-593. https://doi.org/10.1002/we.319.
17.
Tautz-Weinert J, Watson SJ. Using SCADA data for wind turbine condition monitoring–a review. IET Renewable Power Generation. 2016; 11(4): 382-394. https://doi.org/10.1049/iet-rp....
18.
Dao PB, Staszewski WJ, Barszcz T, Uhl T. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. Renewable Energy. 2018; 116: 107-122. https://doi.org/10.1016/j.rene....
19.
Pandit, RK, Infield, D. SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes. IET Renewable Power Generation. 2018; 12(11): 1249-1255. https://doi.org/10.1049/iet-rp....
20.
Ren Y, Qu F, Liu J, Feng J, Li X. A universal modeling approach for wind turbine condition monitoring based on SCADA data. In 2017 6th Data Driven Control and Learning Systems (DDCLS). IEEE. 2017:265-269. https://doi.org/10.1109/DDCLS.....
21.
Astolfi D, Castellani F, Terzi L. Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis. Wind Engineering. 2016; 40(1): 69-85. https://doi.org/10.1177/030952....
22.
Castellani F, Garinei A, Terzi L, Astolfi D, Moretti M, Lombardi A. A new data mining approach for power performance verification of an on-shore wind farm. Diagnostyka. 2013; 14.
23.
Marti-Puig, P, Blanco-MA, Cárdenas JJ, Cusidó J, Solé-Casals J. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies. 2019; 12(3): 453. https://doi.org/10.3390/en1203....
24.
Schlechtingen M, Santos IF, Achiche S. Using data-mining approaches for wind turbine power curve monitoring: a comparative study. IEEE Transactions on Sustainable Energy. 2013; 4(3): 671-679. https://doi.org/10.1109/TSTE.2....
25.
Schlechtingen M, Santos IF. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Applied Soft Computing. 2014; 14: 447-460. https://doi.org/10.1016/j.asoc....
26.
Qiao W, Lu D. A survey on wind turbine condition monitoring and fault diagnosis-Part I: Components and subsystems. IEEE Transactions on Industrial Electronics. 2015; 62(10): 6536-6545. https://doi.org/10.1109/TIE.20....
27.
Cao L, Qian Z, Zareipour H, Wood D, Mollasalehi E, Tian S, Pei Y. Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions. Energies. 2018; 11(12): 3318. https://doi.org/10.3390/en1112....
28.
Mollasalehi E, Wood D, Sun Q. Indicative fault diagnosis of wind turbine generator bearings using tower sound and vibration. Energies 2017; 10(11): 1853. https://doi.org/10.3390/en1011....
30.
Astolfi D, Castellani F, Fravolini ML, Cascianelli S, Terzi L. Precision Computation of Wind Turbine Power Upgrades: An Aerodynamic and Control Optimization Test Case. Journal of Energy Resources Technology. 2019; 141(5): 051205. https://doi.org/10.1115/1.4042....
31.
Astolfi D, Castellani F, Berno F, Terzi L. Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades. Applied Sciences. 2018; 8(12): 2639. https://doi.org/10.3390/app812....
32.
Astolfi D. A study of the impact of pitch misalignment on wind turbine performance. machines. 2019;7(1): 8. https://doi.org/10.3390/machin....
33.
Pozo F, Vidal Y. Wind turbine fault detection through principal component analysis and statistical hypothesis testing. Energies. 2016; 9(1): 3. https://doi.org/10.3390/en9010....
34.
Pozo F, Vidal Y, Salgado Ó. Wind turbine condition monitoring strategy through multiway PCA and multivariate inference. Energies. 2018; 11(4): 749. https://doi.org/10.3390/en1104....
35.
Pozo F, Vidal Y. Damage and fault detection of structures using principal component analysis and hypothesis testing. In Advances in Principal Component Analysis. 2018:137-191. Springer, Singapore. https://doi.org/10.1007/978-98....
36.
Senin N, Moretti M, Leach RK. Shape descriptors and statistical classification on areal topography data for tile inspection in tessellated surfaces. Measurement. 2017;95:82-92. https://doi.org/10.1016/j.meas....
37.
Basha N, Nounou M, Nounou H. Multivariate fault detection and classification using interval principal component analysis. Journal of computational science. 2018;27:1-9. https://doi.org/10.1016/j.jocs....
38.
Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2016; 374(2065), 20150202. https://doi.org/10.1098/rsta.2....
39.
Refaeilzadeh P, Tang L, Liu H. Cross-validation. Encyclopedia of database systems. 2009: 532-538. https://doi.org/10.1007/978-0-....
40.
Zhao Y, Li D, Dong A, Kang D, Lv Q, Shang L. Fault prediction and diagnosis of wind turbine generators using SCADA data. Energies. 2017; 10(8): 1210. https://doi.org/10.3390/en1008....
41.
Astolfi D, Castellani F, Terzi L. A study of wind turbine wakes in complex terrain through RANS simulation and SCADA data. Journal of Solar Energy Engineering. 2018; 140(3): 031001. https://doi.org/10.1115/1.4039....
RELATED ARTICLE
„Podniesienie poziomu naukowego i poziomu umiędzynarodowienia czasopisma Diagnostyka oraz upowszechniania informacji o wynikach badań lub prac rozwojowych poprzez udział uznanych zagranicznych recenzentów w ocenie publikacji - zadanie finansowane w ramach umowy 745/P-DUN/2017. ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę”
© 2006-2023 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.